Datasets:
jblitzar
/
github-python

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# Will loop over resulted elements to get text too to make comparison even more fair otherwise Scrapling will be even faster
return [
element.text for element in Adaptor(
request_html, auto_match=False
).find_by_text('Tipping the Velvet', first_match=True).find_similar(ignore_attributes=['title'])
]
@benchmark
def test_autoscraper(request_html):
# autoscraper by default returns elements text
return AutoScraper().build(html=request_html, wanted_list=['Tipping the Velvet'])
if __name__ == "__main__":
print(' Benchmark: Speed of parsing and retrieving the text content of 5000 nested elements \n')
results1 = {
"Raw Lxml": test_lxml(),
"Parsel/Scrapy": test_parsel(),
"Scrapling": test_scrapling(),
'Selectolax': test_selectolax(),
"PyQuery": test_pyquery(),
"BS4 with Lxml": test_bs4_lxml(),
"MechanicalSoup": test_mechanicalsoup(),
"BS4 with html5lib": test_bs4_html5lib(),
}
display(results1)
print('\n' + "="*25)
req = requests.get('https://books.toscrape.com/index.html')
print(
' Benchmark: Speed of searching for an element by text content, and retrieving the text of similar elements\n'
)
results2 = {
"Scrapling": test_scrapling_text(req.text),
"AutoScraper": test_autoscraper(req.text),
}
display(results2)
# <FILESEP>
import tensorflow as tf
import numpy as np
import random, os
from tensorflow.contrib import slim
import cv2
class ImageData:
def __init__(self, img_height, img_width, channels, augment_flag):
self.img_height = img_height
self.img_width = img_width
self.channels = channels
self.augment_flag = augment_flag
def image_processing(self, filename):
x = tf.read_file(filename)
x_decode = tf.image.decode_jpeg(x, channels=self.channels, dct_method='INTEGER_ACCURATE')
img = tf.image.resize_images(x_decode, [self.img_height, self.img_width])
img = tf.cast(img, tf.float32) / 127.5 - 1
if self.augment_flag :
augment_height = self.img_height + (30 if self.img_height == 256 else int(self.img_height * 0.1))
augment_width = self.img_width + (30 if self.img_width == 256 else int(self.img_width * 0.1))
img = tf.cond(pred=tf.greater_equal(tf.random_uniform(shape=[], minval=0.0, maxval=1.0), 0.5),
true_fn=lambda: augmentation(img, augment_height, augment_width),
false_fn=lambda: img)
return img
def load_test_image(image_path, img_width, img_height, img_channel):
if img_channel == 1 :
img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
else :
img = cv2.imread(image_path, flags=cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, dsize=(img_width, img_height))
if img_channel == 1 :
img = np.expand_dims(img, axis=0)
img = np.expand_dims(img, axis=-1)
else :
img = np.expand_dims(img, axis=0)
img = img/127.5 - 1
return img
def augmentation(image, augment_height, augment_width):
seed = random.randint(0, 2 ** 31 - 1)
ori_image_shape = tf.shape(image)
image = tf.image.random_flip_left_right(image, seed=seed)
image = tf.image.resize_images(image, [augment_height, augment_width])
image = tf.random_crop(image, ori_image_shape, seed=seed)
return image
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)